These averaged weights are then communicated to all clients and this concludes one round of training. The currently supported scheme in FEDn is based on a hierarchical implementation of FedAvg. The choice of hyperparameters, in particular the number of local epochs \(K\) , critically influences the convergence rate and overall training efficiency - a too large value risks local overfitting and slow convergence of the global model, while a small value increases the number of communication rounds. Optimal values will depend both on the model, the number of clients and the data distribution over clients. In a recent study, Pang et al. [1]} investigated the impact of the data heterogeneity problem in FedML frameworks that can affect the performance of the collaboratively trained FedML model. Another study proposed a self-balancing federated learning framework Astraea [2]} to deal with imbalanced clients' data. The presented approach positively effects on the communication and model accuracy. Lim et al. [3]} presented a number of different strategies to address the communication overhead and unreliable network speed.
